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. 2020 May 21:493:110222.
doi: 10.1016/j.jtbi.2020.110222. Epub 2020 Feb 28.

Systems biology of ferroptosis: A modeling approach

Anna Konstorum  1 , Lia Tesfay  2 , Bibbin T Paul  2 , Frank M Torti  3 , Reinhard C Laubenbacher  4 , Suzy V Torti  2
Affiliations

Systems biology of ferroptosis: A modeling approach

Anna Konstorum et al. J Theor Biol. .

Abstract

Ferroptosis is a recently discovered form of iron-dependent regulated cell death (RCD) that occurs via peroxidation of phospholipids containing polyunsaturated fatty acid (PUFA) moieties. Activating this form of cell death is an emerging strategy in cancer treatment. Because multiple pathways and molecular species contribute to the ferroptotic process, predicting which tumors will be sensitive to ferroptosis is a challenge. We thus develop a mathematical model of several critical pathways to ferroptosis in order to perform a systems-level analysis of the process. We show that sensitivity to ferroptosis depends on the activity of multiple upstream cascades, including PUFA incorporation into the phospholipid membrane, and the balance between levels of pro-oxidant factors (reactive oxygen species, lipoxogynases) and antioxidant factors (GPX4). We perform a systems-level analysis of ferroptosis sensitivity as an outcome of five input variables (ACSL4, SCD1, ferroportin, transferrin receptor, and p53) and organize the resulting simulations into 'high' and 'low' ferroptosis sensitivity groups. We make a novel prediction corresponding to the combinatorial requirements of ferroptosis sensitivity to SCD1 and ACSL4 activity. To validate our prediction, we model the ferroptotic response of an ovarian cancer stem cell line following single- and double-knockdown of SCD1 and ACSL4. We find that the experimental outcomes are consistent with our simulated predictions. This work suggests that a systems-level approach is beneficial for understanding the complex combined effects of ferroptotic input, and in predicting cancer susceptibility to ferroptosis.

Keywords: ACSL4; Cancer biology; Discrete model; Ferroptosis; SCD1.

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Figures

Fig. 1.
Fig. 1.
Diagrammatic overview of the multistate model for ferroptosis. Input parameters are model elements that do not change during the course of the simulation (blue outline; dashed line beneath), model variables on the other hand will change concentrations until reaching a steady state (blue outline). Molecular species that are implicitly included in the model but not simulated are also shown (black outline) as well as ferroptosis inducers (green outline). AA: arachidonic acid, LH-P: phospholipid conjugated to polyunsaturated fatty acid, LIP: labile iron pool, LOOH: lipid hydroperoxide, LO•: peroxide radical, LOH: redox-inert alcohol, MUFA: monounsaturated fatty acids, PL: phospholipid, PUFA-CoA: acetylated polyunsaturated fatty acids, ROS: reactive oxygen species, system xc−: cystine/glutamate transporter, SFA: saturated fatty acids.
Fig. 2.
Fig. 2.
Addition of erastin or RSL3 in the baseline model stimulates a ferroptotic response. Steady state values of baseline model variables are shown (a) without and (b) with addition of the ferroptosis-inducers erastin or RSL3. Note that only SLC7A11 responds differently to erastin (no change) vs. RSL3 (lowered). Variables in gray (‘Input’) are external, and thus are set at time t = 0 and stay constant throughout the course of the simulations. The remainder of the variables are classified by types of molecular species or mode of action.
Fig. 3.
Fig. 3.
Effect of ACSL4 knock-out on model behavior (a) without and (b) with addition of RSL3.
Fig. 4.
Fig. 4.
Ferroptosis model output with all combinations of input conditions, sorted by ferroptotic response.
Fig. 5.
Fig. 5.
Ferroptosis model output with all combinations of input conditions, sorted by ferroptotic response, displaying high ferroptosis after erastin treatment.
Fig. 6.
Fig. 6.
Ferroptosis model output with all combinations of input conditions, sorted by ferroptotic response, displaying low ferroptosis after erastin treatment.
Fig. 7.
Fig. 7.
A cellular algorithm for ferroptosis sensitivity. Provided known values of input variables (in blue), one can follow the algorithm to understand how a cell will ‘decide’ whether to undergo spontaneous or drug-induced ferroptosis. This algorithm provides a rule-based summary of the model steady state outcomes.
Fig. 8.
Fig. 8.
Modeling ferroptosis sensitivity in FT-t cells. Ovarian cancer stem cell line FT-t has low p53, high TFRC, and low Fptn. We consider the sensitivity of FT-t cells to erastin under varying SCD and ACSL4. The relationship between conditions experimentally tested and modeled is shown on the right-hand side. For example, the first row shows input conditions identical to the control FT-t row, except the input parameter SCD1 is set to low, which corresponds to a knock down of SCD1 in FT-t cells.
Fig. 9.
Fig. 9.
FT-t cells were transfected with siRNA targeted to SCD1 (siSCD1), ACSL4 (siACSL4), non-targeting siRNA (siNTC) or siRNA targeted to both SCD1 and ACSL4 (siSCD1 + siACSL4) for 48 h. a) Levels of SCD1 mRNA following knockdown of SCD1 and ACSL4 individually or together was Quantified by qRT-PCR. b) Levels of ACSL4 mRNA following knockdown of SCD1 and ACSL4 individually or together was quantified by qRT-PCR. c) Cells were treated with 0.5 to 2 μM RSL3 in the presence or absence of 2 μM ferrostatin-1 for 24 h to assess sensitivity to ferroptosis. Cell viability was measured using calcein-AM. **p<3.6E-29 (siNTC vs siSCD1 and siSCD1 vs siSCD1 + siACSL4) d) Cell death was determined using Trypan-blue exclusion. UNT, untreated controls. *p<2.4E-02 (siNTC vs siSCD1 and siSCD1 vs siSCD1 + siACSL4). Represented are means and standard deviations of three independent experiments, each performed using eight technical replicates per point.
Fig. 10.
Fig. 10.
Transition tables for each modeled species in the multistate rule specification model of ferroptosis.
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